Predicting gastrointestinal drug effects using contextualized metabolic models

Gastrointestinal side effects are among the most common classes of adverse reactions associated with orally absorbed drugs. These effects decrease patient compliance with the treatment and induce undesirable physiological effects. The prediction of drug action on the gut wall based on in vitro data solely can improve the safety of marketed drugs and first-in-human trials of new chemical entities. We used publicly available data of drug-induced gene expression changes to build drug-specific small intestine epithelial cell metabolic models. The combination of measured in vitro gene expression and in silico predicted metabolic rates in the gut wall was used as features for a multilabel support vector machine to predict the occurrence of side effects. We showed that combining local gut wall-specific metabolism with gene expression performs better than gene expression alone, which indicates the role of small intestine metabolism in the development of adverse reactions. Furthermore, we reclassified FDA-labeled drugs with respect to their genetic and metabolic profiles to show hidden similarities between seemingly different drugs. The linkage of xenobiotics to their transcriptomic and metabolic profiles could take pharmacology far beyond the usual indication-based classifications. Author summary The gut wall is the first barrier that encounters orally absorbed drugs, and it substantially modulates the bioavailability of drugs and supports several classes of side effects. We developed context-specific metabolic models of the enterocyte constrained by drug-induced gene expression and trained a machine learning classifier using metabolic reaction rates as features to predict the occurrence of side effects. Additionally, we clustered the compounds based on their metabolic and transcriptomic features to find similarities between their physiological effects. Our work provides a better understanding of the compound physiological effects solely using in vitro data, which can further improve the translation of new chemical entities to clinical trials.